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JacobLinCool/
beat-tracking-alex-20260110

Name: beat-tracking-alex-20260110
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tensorboard

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1709.01507

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Beat Tracking Challenge

A challenge for detecting beats and downbeats in music audio, with a focus on handling dynamic tempo changes common in rhythm game charts.

Goal

The goal is to detect and identify beats and downbeats in audio to assist composers by providing a flexible timing grid when working with samples that have dynamic BPM changes.

Beat: A regular pulse in music (e.g., quarter notes in 4/4 time)
Downbeat: The first beat of each measure (the "1" in counting "1-2-3-4")

This is particularly useful for:

Music production with samples of varying tempos
Rhythm game chart creation and verification
Audio analysis and music information retrieval (MIR)

Dataset

The dataset is derived from Taiko no Tatsujin rhythm game charts, providing high-quality human-annotated beat and downbeat ground truth.

Source: JacobLinCool/taiko-1000-parsed

Split	Tracks	Duration	Description
`train`	~1000	1-3 min each	Training data with beat/downbeat annotations
`test`	~100	1-3 min each	Held-out test set for final evaluation

Data Features

Each example contains:

Field	Type	Description
`audio`	`Audio`	Audio waveform at 16kHz sample rate
`title`	`str`	Track title
`beats`	`list[float]`	Beat timestamps in seconds
`downbeats`	`list[float]`	Downbeat timestamps in seconds

Dataset Characteristics

Dynamic BPM: Many tracks feature tempo changes mid-song
Variable Time Signatures: Common patterns include 4/4, 3/4, 6/8, and more exotic meters
Diverse Genres: Japanese pop, anime themes, classical arrangements, electronic music
High-Quality Annotations: Derived from professional rhythm game charts

Evaluation Metrics

The evaluation considers both timing accuracy and metrical correctness. Models are evaluated on both beat and downbeat detection tasks.

Primary Metrics

1. Weighted F1-Score (Main Ranking Metric)

F1-scores are calculated at multiple timing thresholds (3ms to 30ms), then combined with inverse-threshold weighting:

Threshold	Weight	Rationale
3ms	1.000	Full weight for highest precision
6ms	0.500	Half weight
9ms	0.333	One-third weight
12ms	0.250	...
15ms	0.200
18ms	0.167
21ms	0.143
24ms	0.125
27ms	0.111
30ms	0.100	Minimum weight for coarsest threshold

Formula:

Weighted F1 = Σ(w_t × F1_t) / Σ(w_t)
where w_t = 3ms / t (inverse threshold weighting)

This weighting scheme rewards models that achieve high precision at tight tolerances while still considering coarser thresholds.

2. Continuity Metrics (CMLt, AMLt)

Based on the MIREX beat tracking evaluation protocol using mir_eval:

Metric	Full Name	Description
CMLt	Correct Metrical Level Total	Percentage of beats correctly tracked at the exact metrical level (±17.5% of beat interval)
AMLt	Any Metrical Level Total	Same as CMLt, but allows for acceptable metrical variations (double/half tempo, off-beat)
CMLc	Correct Metrical Level Continuous	Longest continuous correctly-tracked segment at exact metrical level
AMLc	Any Metrical Level Continuous	Longest continuous segment at any acceptable metrical level

Note: Continuity metrics use a default min_beat_time=5.0s (skipping the first 5 seconds) to avoid evaluating potentially unstable tempo at the beginning of tracks.

Metric Interpretation

Metric	What it measures	Good Score
Weighted F1	Precise timing accuracy	> 0.7
CMLt	Correct tempo tracking	> 0.8
AMLt	Tempo tracking (flexible)	> 0.9
CMLc	Longest stable segment	> 0.5

Evaluation Summary

For each model, we report:

Beat Detection:
  Weighted F1: X.XXXX
  CMLt: X.XXXX  AMLt: X.XXXX
  CMLc: X.XXXX  AMLc: X.XXXX

Downbeat Detection:
  Weighted F1: X.XXXX
  CMLt: X.XXXX  AMLt: X.XXXX
  CMLc: X.XXXX  AMLc: X.XXXX

Combined Weighted F1: X.XXXX  (average of beat and downbeat)

Benchmark Results

Results evaluated on 100 tracks from the test set:

Model	Combined F1	Beat F1	Downbeat F1	CMLt (Beat)	CMLt (Downbeat)
Baseline 1 (ODCNN)	0.0765	0.0861	0.0669	0.0731	0.0321
Baseline 2 (ResNet-SE)	0.2775	0.3292	0.2258	0.3287	0.1146

Note: Baseline 2 (ResNet-SE) demonstrates significantly better performance due to larger context window and deeper architecture.

Quick Start

Setup

uv sync

Train Models

bash

# Train Baseline 1 (ODCNN)
uv run -m exp.baseline1.train

# Train Baseline 2 (ResNet-SE)
uv run -m exp.baseline2.train

# Train specific target only (e.g. for Baseline 2)
uv run -m exp.baseline2.train --target beats
uv run -m exp.baseline2.train --target downbeats

Run Evaluation

bash

# Evaluation (replace baseline1 with baseline2 to evaluate the new model)
uv run -m exp.baseline1.eval

# Full evaluation with visualization and audio
uv run -m exp.baseline1.eval --visualize --synthesize --summary-plot

# Evaluate on more samples with custom output directory
uv run -m exp.baseline1.eval --num-samples 50 --output-dir outputs/eval_baseline1

Evaluation Options

Option	Description
Option	Description
--------	-------------
`--model-dir DIR`	Model directory (default: `outputs/baseline1`)
`--num-samples N`	Number of samples to evaluate (default: 20)
`--output-dir DIR`	Output directory (default: `outputs/eval`)
`--visualize`	Generate visualization plots for each track
`--synthesize`	Generate audio files with click tracks
`--viz-tracks N`	Number of tracks to visualize/synthesize (default: 5)
`--time-range START END`	Limit visualization time range (seconds)
`--click-volume FLOAT`	Click sound volume (0.0 to 1.0, default: 0.5)
`--summary-plot`	Generate summary evaluation bar charts

Visualization & Audio Tools

Beat Visualization

Generate plots comparing predicted vs ground truth beats:

uv run -m exp.baseline1.eval --visualize --viz-tracks 10

Output: outputs/eval/plots/track_XXX.png

Click Track Audio

Generate audio files with click sounds overlaid on the original music:

uv run -m exp.baseline1.eval --synthesize

Output files in outputs/eval/audio/:

track_XXX_pred.wav - Original audio + predicted beat clicks (1000Hz beat, 1500Hz downbeat)
track_XXX_gt.wav - Original audio + ground truth clicks (800Hz beat, 1200Hz downbeat)
track_XXX_both.wav - Original audio + both prediction and ground truth clicks

Summary Plot

Generate bar charts summarizing F1 scores and continuity metrics:

uv run -m exp.baseline1.eval --summary-plot

Output: outputs/eval/evaluation_summary.png

Models

Baseline 1: ODCNN

A 10-year-old baseline model: https://ieeexplore.ieee.org/document/6854953.

The original baseline implements the Onset Detection CNN (ODCNN) architecture:

Architecture

Input: Multi-view mel spectrogram (3 window sizes: 23ms, 46ms, 93ms)
CNN Backbone: 3 convolutional blocks with max pooling
Output: Frame-level beat/downbeat probability
Inference: ±7 frames context (±70ms)

Baseline 2: ResNet-SE

Inspired by ResNet-SE: https://arxiv.org/abs/1709.01507.

A modernized architecture designed to capture longer temporal context:

Architecture

Input: Mel spectrogram with larger context
Backbone: ResNet with Squeeze-and-Excitation (SE) blocks
Context: ±50 frames (~1s) window
Features: Deeper network (4 stages) with effective channel attention
Parameters: ~400k (Small & Efficient)

Training Details

Both models use similar training loops:

Optimizer: SGD (Baseline 1) / AdamW (Baseline 2)
Learning Rate: Cosine annealing
Loss: Binary Cross-Entropy
Epochs: 50 (Baseline 1) / 3 (Baseline 2)
Batch Size: 512 (Baseline 1) / 128 (Baseline 2)

Project Structure

exp-onset/
├── exp/
│   ├── baseline1/         # Baseline 1 (ODCNN)
│   │   ├── model.py       # ODCNN architecture
│   │   ├── train.py
│   │   ├── eval.py
│   │   ├── data.py
│   │   └── utils.py
│   ├── baseline2/         # Baseline 2 (ResNet-SE)
│   │   ├── model.py       # ResNet-SE
│   │   ├── train.py
│   │   ├── eval.py
│   │   └── data.py
│   └── data/
│       ├── load.py        # Dataset loading & preprocessing
│       ├── eval.py        # Evaluation metrics (F1, CML, AML)
│       ├── audio.py       # Click track synthesis
│       └── viz.py         # Visualization utilities
├── outputs/
│   ├── baseline1/         # Trained models (Baseline 1)
│   ├── baseline2/         # Trained models (Baseline 2)
│   └── eval/              # Evaluation outputs
│       ├── plots/         # Visualization images
│       ├── audio/         # Click track audio files
│       └── evaluation_summary.png
├── README.md
├── DATASET.md             # Raw dataset specification
└── pyproject.toml

License

This project is for research and educational purposes. The dataset is derived from publicly available rhythm game charts.

JacobLinCool/beat-tracking-alex-20260110

Beat Tracking Challenge

Goal

Dataset

Data Features

Dataset Characteristics

Evaluation Metrics

Primary Metrics

1. Weighted F1-Score (Main Ranking Metric)

2. Continuity Metrics (CMLt, AMLt)

Metric Interpretation

Evaluation Summary

Benchmark Results

Quick Start

Setup

Train Models

Run Evaluation

Evaluation Options

Visualization & Audio Tools

Beat Visualization

Click Track Audio

Summary Plot

Models

Baseline 1: ODCNN

Architecture

Baseline 2: ResNet-SE

Architecture

Training Details

Project Structure

License

JacobLinCool/
beat-tracking-alex-20260110